awesome-machine-learning-on-source-code 4.8k Cool links & research papers related to Machine Learning applied to source code (MLonCode) Learning-to-See-in-the-Dark 4.8k Learning to See in the Dark. CVPR 2018 PyTorch-YOLOv3 4.8k Minimal PyTorch implementation of YOLOv3 first-order-model 4.8...
Creative elearning people coming up with cool logos like the awesome little Litmos monster and the ninja photo of the eLearning Brothers. I particularly liked collages, I think, because they offered the less talented more room for error — mistakes just look like creativity in a collage. Cammy...
alive-progress A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations! 17 filterpy Kalman filtering and optimal estimation library 17 mkl-fft MKL-based FFT transforms for NumPy arrays 17 dictdiffer Dictdiffer is a library that helps you to diff and patch dictionari...
One of the cool things these images show is the groupings of all the shots on a hole, like the tee shots here. And when I see very specific and interactive data like we have here, I know it comes from somewhere that I’m able to see myself. So I figured I should grab that data...
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“Elucidat solves far more problems than we had initially thought that we would use it for. Which is a really cool thing!” Alex Barley Learning Design Manager View Customer Story “The tool has been easily scalable across our organization and has been used by individuals of all skill lev...
Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, ...
The experiment itself might not be running any deep learning model, that is why it is not counted. But it is so cool that I cannot help but put it here.It allows you to build your drum machine with daily sounds like dog bark and baby cry. The data is visualized using T-SNE, ...
The train and test files are identical, so the accuracy is 1.0 as expected. // tennis.txt %%,Yes,No ##,A1,Sunny,Overcast,Rain ##,A2,Hot,Mild,Cool ##,A3,High,Normal ##,A4,Weak,Strong Sunny,Hot,High,Weak,No Sunny,Hot,High,Strong,No Overcast,Hot,High,Weak,Yes Rain,Mild,High,Weak...